A new formula for assessing skeletal age in growing infants and children by measuring carpals and epiphyses of radio and ulna

Luca, Stefano De; Mangiulli, Tatiana; Merelli, Vera; Conforti, Federica; Palacio, Luz Andrea Velandia; Agostini, Susanna; Spinas, Enrico; Cameriere, Roberto

doi:10.1016/j.jflm.2016.01.030

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…The most popular method by Greulich and Pyle (G&P) uses the atlas. 6 Another method is Tanner and Whitehouse method, which depends on a scoring technique and needs a trained observer. 21 More recently, digital methods (computer-aided-diagnosis methods) have been reported to estimate the skeletal age.…”

Section: Region/ Name Of the Bonementioning

confidence: 99%

“…[21][22][23] Example of these digital methods is the one developed by Cameriere et al 24 who described a formula based on the ratio between the area of carpal bones and the total area of carpal bones and distal epiphyses of radius and ulna. 6,[24][25][26] Estimation of skeletal age using MRI or ultrasonography of hand and wrist has been also reported. 27,19 The atlas by G&P contains standard normal radiographs of the left hand of children, and these are presented according to age and gender.…”

Section: Region/ Name Of the Bonementioning

confidence: 99%

“…4 Carpal bones are useful in determining the skeletal age of individuals and assessing their growth status. 5,6 This is particularly of great importance for pediatricians to diagnose metabolic or endocrine disorders. Medicolegally, it is necessary to estimate the bone age in case no birth certificate has been issued.…”

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confidence: 99%

“…Medicolegally, it is necessary to estimate the bone age in case no birth certificate has been issued. 5,6 Standard anatomy literature describes a specific pattern of development of carpal bones. The first bones to ossify are the capitate and hamate, and this occurs during the first year of life.…”

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Time of appearance of ossification centers in carpal bones

Al-Khater

Hegazi

Al-Thani

et al. 2020

SMJ

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A sample of 279 hand/wrist plain radiographs of Saudi children was analyzed. Results: The first bones at the wrist region to appear in Saudi children are the capitate, hamate, and distal epiphysis of the radius, and these appear during the first year of life. The other bones develop subsequently at yearly intervals, and the last one to appear is the pisiform, which arises at the end of the first decade of life. Original Article Conclusion: The sequence of appearance of carpal bones in the Saudi population is similar to what is described in the literature. However, the time of appearance of some of these bones is earlier than that in other populations.

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Section: Region/ Name Of the Bonementioning

confidence: 99%

Section: Region/ Name Of the Bonementioning

confidence: 99%

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Time of appearance of ossification centers in carpal bones

Al-Khater

Hegazi

Al-Thani

et al. 2020

SMJ

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“…In [ 32 ], an ensemble technique has been proposed to extract a set of features from each finger’s joint which is later passed to a linear regressor to predict the final bone age. Instead of focusing on the finger’s features, the work in [ 33 ] has focused on utilizing features from carpals regions to estimate the bone age. On the other hand, methods in [ 34 , 35 ] have employed ANN regressor to determine the bone age.…”

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confidence: 99%

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

et al. 2021

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Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

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